Predicting plasma evolution within a Tokamak reactor is crucial to realizing the goal of sustainable fusion. Capabilities in forecasting the spatio-temporal evolution of plasma rapidly and accurately allow us to quickly iterate over design and control strategies on current Tokamak devices and future reactors. Modelling plasma evolution using numerical solvers is often expensive, consuming many hours on supercomputers, and hence, we need alternative inexpensive surrogate models. We demonstrate accurate predictions of plasma evolution both in simulation and experimental domains using deep learning-based surrogate modelling tools, viz., Fourier neural operators (FNO). We show that FNO has a speedup of six orders of magnitude over traditional solvers in predicting the plasma dynamics simulated from magnetohydrodynamic models, while maintaining a high accuracy (Mean Squared Error in the normalised domain ≈ 10 − 5 ). Our modified version of the FNO is capable of solving multi-variable Partial Differential Equations, and can capture the dependence among the different variables in a single model. FNOs can also predict plasma evolution on real-world experimental data observed by the cameras positioned within the MAST Tokamak, i.e. cameras looking across the central solenoid and the divertor in the Tokamak. We show that FNOs are able to accurately forecast the evolution of plasma and have the potential to be deployed for real-time monitoring. We also illustrate their capability in forecasting the plasma shape, the locations of interactions of the plasma with the central solenoid and the divertor for the full (available) duration of the plasma shot within MAST. The FNO offers a viable alternative for surrogate modelling as it is quick to train and infer, and requires fewer data points, while being able to do zero-shot super-resolution and getting high-fidelity solutions.

Non-linear magnetohydrodynamic (MHD) simulations play an essential role in active research and understanding of tokamak plasmas for the realization of a fusion power plant. The development of MHD codes such as JOREK is a key aspect of this research effort. In this paper, we present an operational version of the full-MHD model implemented in JOREK, a significant advancement from the reduced-MHD model used for previous studies, where assumptions were made on the perpendicular dynamics and the toroidal magnetic field. The final model is presented in detail, and benchmarks are performed using both linear and non-linear simulations, including comparisons between the new full-MHD model of JOREK and the previously extensively studied reduced-MHD model, as well as results from the linear full-MHD code CASTOR3D. For the cases presented, this new JOREK full-MHD model is numerically and physically reliable, even without the use of numerical stabilization methods. Non-linear modeling results of typical tokamak instabilities are presented, including disruption and edge-localized-mode physics, most relevant to current open issues concerning future tokamaks such as ITER and DEMO.

The interaction between Edge-Localized Modes (ELMs) and Resonant Magnetic Perturbations (RMPs) is modeled with the magnetohydrodynamic code JOREK using experimental parameters from ASDEX Upgrade discharges. According to the modeling, the ELM mitigation or suppression is optimal when the amplification of both tearing and peeling-kink responses results in a better RMP penetration. The ELM mitigation or suppression is not only due to the reduction of the pressure gradient but predominantly arises from the toroidal coupling between the ELMs and the RMP-induced mode at the plasma edge, forcing the edge modes to saturate at a low level. The bifurcation from ELM mitigation to ELM suppression is observed when the RMP amplitude is increased. ELM mitigation is characterized by rotating modes at the edge, while the mode locking to RMPs is induced by the resonant braking of the electron perpendicular flow in the ELM suppression regime.

We present a Python-based numerical solver for the two-dimensional dynamic plasma equilibrium problem. We model the time evolution of toroidally symmetric free-boundary tokamak plasma equilibria in the presence of the non-linear magnetohydrodynamic coupling with both currents in the “active” poloidal field coils, with assigned applied voltages, and eddy currents in the tokamak passive structures. FreeGSNKE (FreeGS Newton–Krylov Evolutive) builds and expands on the framework provided by the Python package FreeGS (Free boundary Grad–Shafranov). FreeGS solves the static free-boundary Grad–Shafranov (GS) problem, discretized in space using finite differences, by means of Picard iterations. FreeGSNKE introduces: (i) a solver for the static free-boundary GS problem based on the Newton–Krylov (NK) method, with improved stability and convergence properties; (ii) a solver for the linearized dynamic plasma equilibrium problem; and (iii) a solver for the non-linear dynamic problem, based on the NK method. We propose a novel “staggered” solution strategy for the non-linear problem, in which we make use of a set of equivalent formulations of the non-linear dynamic problem we derive. The alternation of NK solution steps in the currents and in the plasma flux lends this strategy an increased resilience to co-linearity and stagnation problems, resulting in favorable convergence properties. FreeGSNKE can be used for any user-defined tokamak geometry and coil configuration. FreeGSNKE's flexibility and ease of use make it a suitably robust control-oriented simulator of plasma magnetic equilibria. FreeGSNKE is entirely written in Python and easily interfaced with Python libraries, which facilitates machine learning based approaches to plasma control.

ITER is of key importance in the European fusion roadmap as it aims to prove the scientific and technological feasibility of fusion as a future energy source. The EUROfusion consortium of labs within Europe is contributing to the preparation of ITER scientific exploitation and operation and aspires to exploit ITER outcomes in view of DEMO. The paper provides an overview of the major progress obtained recently, carried out in the frame of the new (initiated in 2021) EUROfusion work-package called ‘Preparation of ITER Operation’ (PrIO). The overview paper is directly supported by the eleven EUROfusion PrIO contributions given at the 29th Fusion Energy Conference (16–21 October 2023) London, UK [www.iaea.org/events/fec2023]. The paper covers the following topics: (i) development and validation of tools in support to ITER operation (plasma breakdown/burn-through with evolving plasma volume, new infra-red synthetic diagnostic for off-line analysis and wall monitoring using Artificial Intelligence techniques, synthetic diagnostics development, development and exploitation of multi-machine databases); (ii) R&D for the radio-frequency ITER neutral beam sources leading to long duration of negative deuterium/hydrogen ions current extraction at ELISE and participation in the neutral beam test facility with progress on the ITER source SPIDER, and, the commissioning of the 1 MV high voltage accelerator (MITICA) with lessons learned for ITER; (iii) validation of neutronic tools for ITER nuclear operation following the second JET deuterium–tritium experimental campaigns carried out in 2021 and in 2023 (neutron streaming and shutdown dose rate calculation, water activation and activated corrosion products with advanced fluid dynamic simulation; irradiation of several materials under 14.1 MeV neutron flux etc).